Artificial Intelligence for Smarter Power Systems : Fuzzy Logic and Neural Networks.

Format:

Book

Author/Creator:

Simões, M. Godoy.

Series:

Energy Engineering

Language:

English

Subjects (All):

Smart power grids--Design and construction.

Smart power grids.

Physical Description:

1 online resource (273 pages)

Edition:

1st ed.

Other Title:

Artificial Intelligence for Smarter Power Systems

Place of Publication:

Stevenage : Institution of Engineering & Technology, 2021.

Summary:

This book covers the use of fuzzy logic for power grids. Power systems need to accommodate intermittent renewables and changes in loads while ensuring high power quality. Fuzzy logic uses values between 0 and 1 rather than binary ones, offering advantages in adaptability for energy systems with renewables.

Contents:

Intro

Contents

About the author

Foreword

Preface

1. Introduction

1.1 Renewable-energy-based generation is shaping the future of power systems

1.2 Power electronics and artificial intelligence (AI) allow smarter power systems

1.3 Power electronic, artificial intelligence (AI), and simulations will enable optimal operation of renewable energy systems

1.4 Engineering, modeling, simulation, and experimental models

1.5 Artificial intelligence will play a key role to control microgrid bidirectional power flow

1.6 Book organization optimized for problem-based learning strategies

2. Real-time simulation applications for future power systems and smart grids

2.1 The state of the art and the future of real-time simulation

2.2 Real-time simulation basics and technological considerations

2.3 Introduction to the concepts of hardware-in-the-loop testing

2.4 RTS testing of smart inverters

2.5 RTS testing of wide area monitoring, control, and protection systems (WAMPACS)

2.6 Digital twin concepts and real-time simulators

3. Fuzzy sets

3.1 What is an intelligent system

3.2 Fuzzy reasoning

3.3 Introduction to fuzzy sets

3.4 Introduction to fuzzy logic

3.4.1 Defining fuzzy sets in practical applications

3.5 Fuzzy sets kernel

4. Fuzzy inference: rule based and relational approaches

4.1 Fuzzification, defuzzification, and fuzzy inference engine

4.2 Fuzzy operations in different universes of discourse

4.3 Mamdani's rule-based Type 1 fuzzy inference

4.4 Takagi-Sugeno-Kang (TSK), Type 2 fuzzy inference, parametric fuzzy, and relational-based

4.5 Fuzzy model identification and supervision control

5. Fuzzy-logic-based control

5.1 Fuzzy control preliminaries

5.2 Fuzzy controller heuristics

5.3 Fuzzy logic controller design.

5.4 Industrial fuzzy control supervision and scheduling of conventional controllers

6. Feedforward neural networks

6.1 Backpropagation algorithm

6.2 Feedforward neural networks-a simple binary classifier

6.3 Artificial neural network architecture-from the McCulloch-Pitts neuron to multilayer feedforward networks

6.4 Neuron activation transfer functions

6.5 Data processing for neural networks

6.6 Neural-network-based computing

7. Feedback, competitive, and associative neural networks

7.1 Feedback networks

7.2 Linear Vector Quantization network

7.3 Counterpropagation network

7.4 Probabilistic neural network

7.5 Industrial applicability of artificial neural networks

8. Applications of fuzzy logic and neural networks in power electronics and power systems

8.1 Fuzzy logic and neural-network-based controller design

8.2 Fuzzy-logic-based function optimization

8.3 Fuzzy-logic-and-neural-network-based function approximation

8.4 Neuro-fuzzy ANFIS-adaptive neural fuzzy inference system

8.5 AI-based control systems for smarter power systems

8.6 Artificial intelligence for control systems

9. Deep learning and big data applications in electrical power systems

9.1 Big data analytics, data science, engineering, and power quality

9.2 Big data for smart-grid control

9.3 Online monitoring of diverse time scale fault events for non-intentional islanding

9.4 Smart electrical power systems and deep learning features

9.5 Classification, regression, and clustering with neural networks

9.6 Classification building blocks: Instar and Outstar

9.7 Classification principles with convolutional neural networks

9.8 Principles of recurrent neural networks

Bibliography

Index.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-83724-565-7

1-83953-001-4

OCLC:

1264476528